

'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
					import PIL

if is_torch_available():
					import torch


_lowerCamelCase      					=     logging.get_logger(__name__)
class 					_snake_case		(lowercase__):
			__A  :	Optional[int]        =["pixel_values"]






			def __init__(  self			,_snake_case = True			,_snake_case = None			,_snake_case = PILImageResampling.BILINEAR			,_snake_case = True			,_snake_case = 1 / 2_55			,_snake_case = True			,_snake_case = None			,_snake_case = True			,**_snake_case			,):
				super().__init__(**SCREAMING_SNAKE_CASE_     )
				UpperCAmelCase_     :      Tuple						=  size if size is not None else {"shortest_edge": 2_24}
				UpperCAmelCase_     :      Dict						=  get_size_dict(SCREAMING_SNAKE_CASE_			,default_to_square=SCREAMING_SNAKE_CASE_     )
				UpperCAmelCase_     :      Optional[int]						=  crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
				UpperCAmelCase_     :      Tuple						=  get_size_dict(SCREAMING_SNAKE_CASE_			,param_name="crop_size"     )

				UpperCAmelCase_     :      str						=  do_resize
				UpperCAmelCase_     :      int						=  size
				UpperCAmelCase_     :      Dict						=  resample
				UpperCAmelCase_     :      List[Any]						=  do_rescale
				UpperCAmelCase_     :      Union[str, Any]						=  rescale_factor
				UpperCAmelCase_     :      str						=  do_center_crop
				UpperCAmelCase_     :      Union[str, Any]						=  crop_size
				UpperCAmelCase_     :      str						=  do_flip_channel_order






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case = PIL.Image.BILINEAR			,_snake_case = None			,**_snake_case			,):
				UpperCAmelCase_     :      Optional[Any]						=  get_size_dict(SCREAMING_SNAKE_CASE_			,default_to_square=SCREAMING_SNAKE_CASE_     )
				if "shortest_edge" not in size:
					raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''     )
				UpperCAmelCase_     :      List[Any]						=  get_resize_output_image_size(SCREAMING_SNAKE_CASE_			,size=size["shortest_edge"]			,default_to_square=SCREAMING_SNAKE_CASE_     )
				return resize(SCREAMING_SNAKE_CASE_			,size=SCREAMING_SNAKE_CASE_			,resample=SCREAMING_SNAKE_CASE_			,data_format=SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case = None			,**_snake_case			,):
				UpperCAmelCase_     :      Any						=  get_size_dict(SCREAMING_SNAKE_CASE_     )
				if "height" not in size or "width" not in size:
					raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''     )
				return center_crop(SCREAMING_SNAKE_CASE_			,size=(size["height"], size["width"])			,data_format=SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case = None			,**_snake_case			,):
				return rescale(SCREAMING_SNAKE_CASE_			,scale=SCREAMING_SNAKE_CASE_			,data_format=SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None     ):
				return flip_channel_order(SCREAMING_SNAKE_CASE_			,data_format=SCREAMING_SNAKE_CASE_     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = ChannelDimension.FIRST			,**_snake_case			,):
				UpperCAmelCase_     :      int						=  do_resize if do_resize is not None else self.do_resize
				UpperCAmelCase_     :      Optional[Any]						=  resample if resample is not None else self.resample
				UpperCAmelCase_     :      int						=  do_rescale if do_rescale is not None else self.do_rescale
				UpperCAmelCase_     :      Optional[int]						=  rescale_factor if rescale_factor is not None else self.rescale_factor
				UpperCAmelCase_     :      Any						=  do_center_crop if do_center_crop is not None else self.do_center_crop
				UpperCAmelCase_     :      Tuple						=  (
				    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
				)

				UpperCAmelCase_     :      str						=  size if size is not None else self.size
				UpperCAmelCase_     :      Optional[int]						=  get_size_dict(SCREAMING_SNAKE_CASE_			,default_to_square=SCREAMING_SNAKE_CASE_     )
				UpperCAmelCase_     :      List[str]						=  crop_size if crop_size is not None else self.crop_size
				UpperCAmelCase_     :      Dict						=  get_size_dict(SCREAMING_SNAKE_CASE_			,param_name="crop_size"     )

				UpperCAmelCase_     :      Tuple						=  make_list_of_images(SCREAMING_SNAKE_CASE_     )

				if not valid_images(SCREAMING_SNAKE_CASE_     ):
					raise ValueError(
					    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
					    "torch.Tensor, tf.Tensor or jax.ndarray."     )

				if do_resize and size is None:
					raise ValueError("Size must be specified if do_resize is True."     )

				if do_rescale and rescale_factor is None:
					raise ValueError("Rescale factor must be specified if do_rescale is True."     )

				if do_center_crop and crop_size is None:
					raise ValueError("Crop size must be specified if do_center_crop is True."     )

				# All transformations expect numpy arrays.
				UpperCAmelCase_     :      Union[str, Any]						=  [to_numpy_array(SCREAMING_SNAKE_CASE_     ) for image in images]

				if do_resize:
					UpperCAmelCase_     :      List[Any]						=  [self.resize(image=SCREAMING_SNAKE_CASE_			,size=SCREAMING_SNAKE_CASE_			,resample=SCREAMING_SNAKE_CASE_     ) for image in images]

				if do_center_crop:
					UpperCAmelCase_     :      Dict						=  [self.center_crop(image=SCREAMING_SNAKE_CASE_			,size=SCREAMING_SNAKE_CASE_     ) for image in images]

				if do_rescale:
					UpperCAmelCase_     :      int						=  [self.rescale(image=SCREAMING_SNAKE_CASE_			,scale=SCREAMING_SNAKE_CASE_     ) for image in images]

				# the pretrained checkpoints assume images are BGR, not RGB
				if do_flip_channel_order:
					UpperCAmelCase_     :      Optional[Any]						=  [self.flip_channel_order(image=SCREAMING_SNAKE_CASE_     ) for image in images]

				UpperCAmelCase_     :      Optional[Any]						=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_     ) for image in images]

				UpperCAmelCase_     :      str						=  {"pixel_values": images}
				return BatchFeature(data=SCREAMING_SNAKE_CASE_			,tensor_type=SCREAMING_SNAKE_CASE_     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None     ):
				UpperCAmelCase_     :      Union[str, Any]						=  outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
					if len(SCREAMING_SNAKE_CASE_     ) != len(SCREAMING_SNAKE_CASE_     ):
						raise ValueError(
						    "Make sure that you pass in as many target sizes as the batch dimension of the logits"     )

					if is_torch_tensor(SCREAMING_SNAKE_CASE_     ):
						UpperCAmelCase_     :      Dict						=  target_sizes.numpy()

					UpperCAmelCase_     :      Optional[Any]						=  []

					for idx in range(len(SCREAMING_SNAKE_CASE_     )     ):
						UpperCAmelCase_     :      Optional[Any]						=  torch.nn.functional.interpolate(
						    logits[idx].unsqueeze(dim=0     )			,size=target_sizes[idx]			,mode="bilinear"			,align_corners=SCREAMING_SNAKE_CASE_     )
						UpperCAmelCase_     :      int						=  resized_logits[0].argmax(dim=0     )
						semantic_segmentation.append(SCREAMING_SNAKE_CASE_     )
				else:
					UpperCAmelCase_     :      List[Any]						=  logits.argmax(dim=1     )
					UpperCAmelCase_     :      Optional[int]						=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]     )]

				return semantic_segmentation






"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''CLIPImageProcessor'''
			lowercase        =     ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	int=None      ,SCREAMING_SNAKE_CASE_  :	str=None      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  None
				if "feature_extractor" in kwargs:
					warnings.warn(
					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
					    ''' instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
					lowerCAmelCase												=  kwargs.pop('''feature_extractor'''			)

				lowerCAmelCase												=  image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
					raise ValueError('''You need to specify an `image_processor`.'''			)
				if tokenizer is None:
					raise ValueError('''You need to specify a `tokenizer`.'''			)

				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def __call__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	List[str]=None      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=None      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=None      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	List[Any]:





				"""simple docstring"""


				if text is None and images is None:
					raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if images is not None:
					lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if text is not None and images is not None:
					lowerCAmelCase												=  image_features.pixel_values
					return encoding
				elif text is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_			)      ,tensor_type=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Optional[Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,*SCREAMING_SNAKE_CASE_  :	int      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


